This invention relates to the control of a multivariable, time-varying, industrial process that exhibits either linear or nonlinear response characteristics and more particularly the on-line execution of an adaptive, linear model predictive controller derived from a rigorous, nonlinear model of such processes.
There are many multivariable, time-varying, industrial processes that exhibit either linear or nonlinear response characteristics. Several examples of such processes are a distillation column, a separator train, a catalytic cracking unit, a chemical reactor, and a utility boiler. Control of these processes using traditional multivariable control techniques with fixed model representations is difficult because of the variable process conditions that result from either process nonlinearities, measured or unmeasured disturbances, feedstock changes, or operator-induced changes.
The standard practice for advanced industrial process control in processes of the type described above is to use linear, multivariable, model predictive controller (MPC) software. See for example, the Setpoint, Inc. product literature dated 1993 entitled xe2x80x9cSMC-Idcom: A State-of-the-Art Multivariable Predictive Controllerxe2x80x9d; the DMC Corp. product literature dated 1994 entitled xe2x80x9cDMC(trademark): Technology Overviewxe2x80x9d; the Honeywell Inc. product literature dated 1995 entitled xe2x80x9cRMPCT Concepts Referencexe2x80x9d; and Garcia, C. E. and Morshedi, A. M. (1986), xe2x80x9cQuadratic Programming Solution of Dynamic Matrix Control (QDMC)xe2x80x9d, Chem. Eng. Commun. 46: 73-87. The typical MPC software allows for model scheduling (i.e. changing the model gains and/or dynamics) to improve control performance when operating on a nonlinear and/or time-varying process. The controller uses new models that are generally calculated in an off-line mode, or may be calculated by an adaptive algorithm that uses recent operating data.
If the models are calculated off-line, then the controller requires additional on-line logic to determine which set of model parameters should be used at the current time. This logic is often difficult to develop since it may depend on numerous operating variables. For large problems there may be a significant number of different models required in order to improve performance. Furthermore, there is a possibility that invoking a particular set of model parameters for certain operating points will eventually lead to unstable operation. Thus, the off-line model identification task is extremely time-consuming and expensive and its implementation on-line is not proven robust over wide operating ranges. Therefore, in most applications a simplified set of models is defined (e.g. high feed rate and low feed rate models; or winter and summer operation models).
The control performance using a simplified set of models is not much better than what can be achieved using a single model. In fact, controllers developed using this methodology do not usually remain on-line for processes demonstrating a high degree of non-linearity.
Adaptive model identification is another way to modify linear models to capture current operating conditions. This procedure uses recent operating data to automatically adjust the models on-line. One of the biggest problems of this approach is inherent from the dual control principle, which essentially states that the uncertainty in model identification increases as the control performance improves (i.e. as the control uncertainty decreases). Thus, it is always much more difficult to obtain accurate new models when the controller is running since the input signals are correlated and the output signals have small deviations. In fact, the identification process often fails because of the loss of persistent excitation in the input signals. See Astrom, K. J. and Wittenmark, B. (1995) xe2x80x9cAdaptive Controlxe2x80x9d, Addison-Wesley Publishing Company, Inc., pp. 69-70 and 473-477, for a detailed discussion of this problem. This inherent mathematical problem limits the success of adaptive, linear model identification. Current algorithms use various methods to turn off or heavily filter the parameter changes in the adaptive identification. Otherwise, poor models could be selected because of the high model uncertainty and ultimately the control performance would suffer.
There are some types of industrial processes that cause additional problems for empirical model identification methods. For example, a batch process could operate with multiple product runs during which the model gains change by orders of magnitude (e.g. polyethylene, polypropylene processing), yet the batches may not reach equilibrium. Linear, empirical methods cannot obtain accurate models for such processes since the necessary operational data is not available.
Model identification problems also occur for processes that do not allow plant testing near dangerous or unstable operating conditions (e.g. catalytic cracking, reactors). Controllers for such processes try to prevent violation of constraint limits that could lead to unstable behavior, however the benefits often increase by operating closer to the limits. Therefore to improve performance and maximize benefits, the controller needs to have multiple models available for all the operating regions. This is not possible with linear, empirical methods. A rigorous, nonlinear process simulation model could, however, provide these multiple models for the controller.
The same type of model identification problems can occur for non-dangerous processes that have product specification limits (e.g. distillation columns). The plant manager does not want to generate off-spec product, so the testing must be conducted such that the purity remains within acceptable limits. However, greater benefits are typically obtained by operating closer to these limits. Thus, the empirical methods cannot generate accurate models for operating points that are close to or beyond the limits. High-purity separation processes present the most significant modeling problems as very nonlinear behavior may occur over the different operating regions.
Another disadvantage of empirical MPC identification methods occurs when the controller is used together with real-time optimization (RTO) since the models are not consistent. RTO generally uses a more rigorous, nonlinear model while MPC is either using a fixed linear model obtained from plant testing, or an adaptive, linear model obtained from on-line data. In either case, the MPC models are not derived from the RTO model (nor vice versa), so this inconsistency often leads to significant performance degradation. See for example the article by Y. Z. Friedman entitled xe2x80x9cWhat""s wrong with unit closed loop optimization?xe2x80x9d which appeared in the October 1995 issue of Hydrocarbon Processing at pp. 107-116.
Another means of treating nonlinear, time-varying processes is to use robust control methods. The idea behind robust control is to use a single process model, but to tune the controller by accounting for modeling uncertainties. The control design is tested against a range of expected operating conditions and is retuned until the performance is fairly consistent (or robust) over all conditions. The robust tuning method reduces the sensitivity of the controller to model error. Honeywell has applied this technique to its RMPCT (Robust Multivariable Predictive Control Technology) product as described in the product literature referenced above.
However, there are some drawbacks to using robust control techniques. In particular, by restricting the controller to a single linear model, robust tuning often results in slow, sluggish control performance over the typical operating range because it attempts to improve the worst-case performance. Thus, the robust tuning often degrades performance during periods of small modeling error. This occurs because the expected range of modeling uncertainties is often too large for a single model and single set of tuning parameters. It is possible to vary the tuning, but this becomes a difficult on-line implementation task. The real solution is to allow multiple models for processes with significant nonlinearities and/or time-varying behavior.
The patent prior art includes many examples of the use of model-based control systems employing both linear and nonlinear methodologies for control. Most of the prior art in MPC refers to linear controllers [see for example, U.S. Pat. No. 4,349,869 to Prett et al.; U.S. Pat. No. 4,616,308 to Morshedi et al.; U.S. Pat. No. 5,351,184 to Lu et al.; and U.S. Pat. No. 5,572,420 to Lu]. To handle nonlinear, time-varying processes, these controllers may use gain scheduling, adaptive model estimation, or robust control tuning. These approaches typically encounter implementation problems and/or performance degradation for the types of processes and operating conditions described previously.
There have been a few patents issued for nonlinear model-based control methodologies. In particular, U.S. Pat. No. 5,260,865 to Beauford et al. describes a nonlinear model-based control strategy for a distillation process which employs a nonlinear model to compute liquid and vapor flow rates required for composition control. Sanner and Slotine (U.S. Pat. No. 5,268,834) employ a neural network together with other nonlinear control strategies to provide adaptive control of a plant. Bartusiak and Fontaine (U.S. Pat. No. 5,682,309) developed a reference synthesis technique in which the controller attempts to make a nonlinear plant follow a specified reference trajectory. U.S. Pat. No. 5,740,033 to Wassick et al. describes an MPC that employs a real-time executive sequencer and an interactive modeler to find the optimized set of control changes for a nonlinear process.
Large, nonlinear control problems are difficult to solve in an on-line operating environment. The solver must be fast and robust. It is unknown whether these inventions have been able to achieve successful implementations of on-line, nonlinear control.
The invention is a methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations. Thus, a state space MPC simplifies the translation of the linearized simulation equations to the modeling format required by the controller. However, the state space methodology also allows the calculation of various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients. Therefore, the methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller.
The methodology also includes various modules that improve reliability and performance. For example, there is a data pretreatment module used to pre-process the plant measurements for gross error detection. A data reconciliation and parameter estimation module is then used to correct for instrumentation errors and to adjust model parameters based on current operating conditions. The full-order state space model can be reduced by the order reduction module to obtain fewer states for the controller model. Automated MPC tuning is also provided to improve control performance.
The invention is also embodied as a method for controlling a process that comprises:
a) receiving plant measurement variables from a regulatory control system;
b) pretreating said plant measurement variables;
c) reconciling said pretreated plant measurement variables;
d) using said reconciled and pretreated plant measurement variables to update one or more variables of each submodel of a nonlinear model, said nonlinear model having two or more of said submodels, each of said two or more submodels having a predetermined one of two or more model predictive controllers associated therewith;
e) converting at least one updated submodel of said updated nonlinear model to a linear submodel when a change in said one or more of said updated submodel variables has exceeded a predetermined threshold, said linear submodel for operating said associated one of said two or more controllers;
f) using said nonlinear model in a real time optimizer to compute targets for all of said two or more model predictive controllers, a predetermined subset of said computed targets associated with a respective one of said two or more controllers;
g) passing each of said predetermined subsets of said computed targets associated with a respective one of said two or more model predictive controllers to said associated one of said two or more controllers;
h) converting said at least one linearized submodel to a full order state space submodel;
i) creating from said full order state space submodel a state space submodel having fewer states than said full order state space submodel;
j) converting said fewer states state space submodel to a MPC format submodel; and
k) evaluating the performance of said MPC format submodel with the tuning for a presently existing submodel of said process in said associated one of said two or more model predictive controllers versus the performance of said presently existing submodel with said tuning and either:
passing said MPC format submodel with said presently existing submodel tuning to said associated one of said two or more model predictive controllers when said performance evaluation of said MPC format submodel exceeds a first predetermined limit; or
computing new tuning for said MPC format submodel when said performance evaluation of said MPC format submodel falls below said first predetermined limit and repeating said evaluations; or
returning said MPC format submodel to said creating a MPC format submodel having fewer states than said full order state space submodel to change the number of states in said MPC format submodel when said performance of said MPC format submodel falls below said first predetermined limit.
The invention is also further embodied as a method for controlling a process that comprises:
a) receiving plant measurement variables from a regulatory control system;
b) pretreating said plant measurement variables;
c) reconciling said pretreated plant measurement variables;
d) using said reconciled and pretreated plant measurement variables to update one or more variables of each submodel of a nonlinear model, said nonlinear model having two or more of said submodels, each of said two or more submodels having a predetermined one of two or more model predictive controllers associated therewith;
e) converting at least one updated submodel of said updated nonlinear model to a linear submodel when a change in one or more model prediction errors in an associated one of one or more MPC format submodels currently operational in an associated one of said two or more model predictive controllers has exceeded a predetermined threshold, said linear submodel for operating said associated one of said two or more controllers;
f) using said nonlinear model in a real time optimizer to compute targets for all of said two or more model predictive controllers, a predetermined subset of said computed targets associated with a respective one of said two or more controllers;
g) passing each of said predetermined subsets of said computed targets associated with a respective one of said two or more model predictive controllers to said associated one of said two or more controllers; and
h) converting said at least one linearized submodel to a full order state space submodel;
i) creating from said full order state space submodel a state space submodel having fewer states than said full order state space submodel;
j) converting said fewer states state space submodel to said MPC format submodel; and
k) evaluating the performance of said MPC format submodel with the tuning for a presently existing submodel of said process in said associated one of said two or more model predictive controllers versus the performance of said presently existing submodel with said tuning and either:
passing said MPC format submodel with said presently existing submodel tuning to said associated one of said two or more model predictive controllers when said performance evaluation of said MPC format submodel exceeds a first predetermined limit; or
computing new tuning for said MPC format submodel when said performance evaluation of said MPC format submodel falls below said first predetermined limit and repeating said evaluations; or
returning said MPC format submodel to said creating a MPC format submodel having fewer states than said full order state space submodel to change the number of states in said MPC format submodel when said performance of said MPC format submodel falls below said first predetermined limit.
The invention is also embodied further as a method for controlling a process that comprises:
a) receiving plant measurement variables from a regulatory control system;
b) pretreating said plant measurement variables;
c) reconciling said pretreated plant measurement variables;
d) using said reconciled and pretreated plant measurement variables to update one or more variables of each submodel of a nonlinear model, said nonlinear model having two or more of said submodels, each of said two or more submodels having a predetermined one of two or more model predictive controllers associated therewith;
e) converting at least one updated submodel of said updated nonlinear model to a linear submodel when a change in said one or more of said updated submodel variables has exceeded a predetermined threshold, said linear submodel for operating said associated one of said two or more controllers;
f) using said nonlinear model in a real time optimizer to compute targets for all of said two or more model predictive controllers, a predetermined subset of said computed targets associated with a respective one of said two or more controllers;
g) passing each of said predetermined subsets of said computed targets associated with a respective one of said two or more model predictive controllers to said associated one of said two or more controllers; and
h) passing said linear model to said associated one of said two or more controllers comprising:
evaluating the performance of said MPC format submodel with the tuning for a presently existing submodel of said process in said associated one of said two or more model predictive controllers versus the performance of said presently existing submodel with said tuning and either:
passing said MPC format submodel with said presently existing submodel tuning to said associated one of said two or more model predictive controllers when said performance evaluation of said MPC format submodel exceeds a first predetermined limit; or
computing new tuning for said MPC format submodel when said performance evaluation of said MPC format submodel falls below said first predetermined limit and repeating said evaluating.
The invention is further embodied as a method for controlling a process that comprises:
a) receiving plant measurement variables from a regulatory control system;
b) pretreating said plant measurement variables;
c) reconciling said pretreated plant measurement variables;
d) using said reconciled and pretreated plant measurement variables to update one or more variables of each submodel of a nonlinear model, said nonlinear model having two or more of said submodels, each of said two or more submodels having a predetermined one of two or more model predictive controllers associated therewith;
e) converting at least one updated submodel of said updated nonlinear model to a linear submodel when a change in one or more model prediction errors in an associated one of one or more MPC format submodels currently operational in an associated one of said two or more model predictive controllers has exceeded a predetermined threshold, said linear submodel for operating said associated one of said two or more controllers;
f) using said nonlinear model in a real time optimizer to compute targets for all of said two or more model predictive controllers, a predetermined subset of said computed targets associated with a respective one of said two or more controllers;
g) passing each of said predetermined subsets of said computed targets associated with a respective one of said two or more model predictive controllers to said associated one of said two or more controllers; and
h) passing said linear model to said associated one of said two or more controllers comprising:
evaluating the performance of said MPC format submodel with the tuning for a presently existing submodel of said process in said associated one of said two or more model predictive controllers versus the performance of said presently existing submodel with said tuning and either:
passing said MPC format submodel with said presently existing submodel tuning to said associated one of said two or more model predictive controllers when said performance evaluation of said MPC format submodel exceeds a first predetermined limit; or
computing new tuning for said MPC format submodel when said performance evaluation of said MPC format submodel falls below said first predetermined limit and repeating said evaluating.